In this work, we relied on electrophysiological methods to characterize the processing stages that are affected by the presence of regularity in a visual search task. EEG was recorded for 72 participants while they completed a visual search task. Depending on the group, the task contained a consistent-mapping condition, a random-mapping condition, or both consistent and random conditions intermixed (mixed group). Contrary to previous findings, the control groups allowed us to demonstrate that the contextual cueing effect that was observed in the mixed group resulted from interference, not facilitation, to the target selection, response selection, and response execution processes (N2-posterior-contralateral, stimulus-locked lateralized readiness potential [LRP], and response-locked LRP components). When the regularity was highly valid (consistent-only group), the presence of regularity drove performance beyond general practice effects, through facilitation in target selection and response selection (N2-posterior-contralateral and stimulus-locked LRP components). Overall, we identified two distinct effects created by the presence of regularity: a global effect of validity that dictates the degree to which all information is taken into account and a local effect of activating the information on every trial. We conclude that, when considering the influence of regularity on behavior, it is vital to assess how the overall reliability of the incoming information is affected.
The brain integrates streams of sensory input and builds accurate predictions, while arriving at stable percepts under disparate time scales. This stochastic process bears different dynamics for different people, yet statistical learning (SL) currently averages out, as noise, individual fluctuations in data streams registered from the brain as the person learns. We here adopt the motor systems perspective to reframe SL. Specifically, we rethink this problem using the demands that the person's brain faces to predict, and control variations in biorhythmic activity akin to those present in bodily motions. This new approach harnesses gross data as the important signals, to reassess how individuals learn predictive information in stable and unstable environments. We find two types of learners: narrow-variance learners, who retain explicit knowledge of the regularity embedded in the stimuli -the goal. They seem to use an error-correction strategy steadily present in both stable and unstable cases. In contrast, broad-variance learners emerge only in the unstable environment. They undergo an initial period of memoryless learning characterized by a gamma process that starts out exponentially distributed but converges to Gaussian. We coin this mode exploratory, preceding the more general error-correction mode characterized by skewed-to-symmetric distributions and higher signal content from the start. Our work demonstrates that statistical learning is a highly dynamic and stochastic process, unfolding at different time scales, and evolving distinct learning strategies on demand.
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